Asset and liability management (ALM) is still a cornerstone of banking. But when market conditions shift quickly, as they increasingly do, it no longer reflects how banks actually experience and manage risk, capital and profitability.
The environment has changed. Volatility is now persistent rather than episodic. Regulatory expectations continue to rise. Customer behavior, funding markets and interest rate dynamics can shift quickly. As a result, credit, liquidity, market and capital risks are tightly intertwined. A decision made in one part of the organization can ripple across the rest of it – sometimes immediately, sometimes only becoming visible under stress.
For banking leaders, the question is no longer whether risks are connected, but whether the organization is equipped to see and manage those connections in time.
Despite this reality, many banks still rely on ways of working that examine these dimensions separately. That often leads to slower decision cycles, misaligned assumptions and limited visibility into the trade‑offs that matter most when conditions change.
Increasingly, banks are responding by rethinking how they steer their finances, moving toward a more holistic, analytics‑driven and AI‑enabled way of managing assets and liabilities as one interconnected system.
Moving beyond siloed ALM
Traditional ALM frameworks were designed for a more stable world, and they worked well in that context. Such frameworks focused on net interest income, interest rate risk in the banking book and high‑level liquidity metrics, all typically reviewed in periodic cycles and based on relatively static assumptions.
That perspective is no longer sufficient. In today’s environment, banks need a clearer and more continuous understanding of how changes in credit quality influence funding costs, liquidity buffers and capital usage. Banks need to see how macroeconomic uncertainty and regulatory stress scenarios play out across the institution; whether finance, risk, and treasury teams are working from the same assumptions; and how quickly decisions can be explained, justified and adjusted when circumstances shift.
More advanced balance sheet practices bring risk and finance together around a shared view of assets and liabilities. Instead of optimizing individual metrics in isolation, banks can see the trade‑offs more clearly. A funding action that improves short‑term liquidity, for example, may increase capital pressure under stress. Deterioration in credit quality may affect profitability, funding plans and regulatory ratios simultaneously. Seeing these connections early can materially change outcomes.
A unified view across risk, finance and regulation
At its heart, modern balance sheet steering is about integration, but not integration for its own sake. The real objective is better decision‑making.
When balance‑sheet‑relevant disciplines operate on consistent data, aligned scenarios and shared assumptions, banks are better able to assess pricing, funding and hedging decisions from multiple perspectives at once. Banks can anticipate knock‑on effects before they surface in results or ratios; reduce time‑consuming reconciliation between finance, risk and regulatory reporting; and engage more confidently with supervisors, particularly during stress testing exercises.
The payoff is not higher report volume. It provides clearer insight and stronger decisions, especially when uncertainty is high.
A cautionary real‑world example: Silicon Valley Bank
Recent history offers a clear reminder that when risks interact faster than governance can respond, the consequences can unfold quickly. The collapse of Silicon Valley Bank (SVB) in March 2023 was not driven by credit losses, but by a combination of interest rate, liquidity and concentration risks that proved difficult to manage in an integrated way.
SVB had invested a large share of its rapidly growing deposit base in long‑dated, fixed‑income securities during a prolonged period of low interest rates. When rates rose sharply, the market value of those assets declined, eroding economic capital.
At the same time, the bank’s highly concentrated and largely uninsured depositor base turned out to be far more sensitive – and far faster to react – than traditional liquidity assumptions anticipated.
An extremely rapid run on deposits followed – one that existing ALM and liquidity frameworks could not absorb.
Post-event analyses from regulators and independent experts point to a common theme: banks identified and monitored risks, but did not fully understand or act on them in combination. Banks assessed interest rate risk, liquidity risk, funding concentration and governance in silos, limiting their visibility into how quickly and widely stress could spread.
SVB’s failure shows that even banks with sophisticated analytics become vulnerable when they fail to evaluate balance-sheet decisions holistically under realistic and severe scenarios.
The lesson is not that ALM is obsolete, but that ALM must evolve. In an environment shaped by volatile rates, digital‑speed deposit flows and heightened scrutiny, banks need a more integrated, forward‑looking and scenario‑driven way of understanding how interest rate, liquidity and funding risks interact and amplify each other across assets and liabilities.
How AI is changing the game
Advanced analytics has long played a central role in how banks manage assets and liabilities. What is changing now is the impact of AI, particularly generative AI and agent‑based techniques.
Machine learning is already improving forecasts, behavioral models and scenario analysis. AI now extends this further. Generative AI makes it easier to interact with complex analytics using natural language, while also helping explain results clearly to senior management and regulators. Agentic AI introduces intelligent analytical agents that continuously monitor signals across portfolios, explore alternative actions and suggest responses as market or regulatory conditions evolve.
This marks a move away from periodic, backward‑looking analysis toward a more continuous, adaptive approach to steering financial outcomes – one that actively supports both strategic choices and day‑to‑day decisions.
What banks gain in practice
Banks that embrace a more integrated, AI‑enabled approach to managing assets and liabilities see tangible business‑level benefits. Profitability improves as pricing, funding and hedging decisions become better informed. Resilience increases as leaders gain a clearer understanding of how stress scenarios affect the institution as a whole. Regulatory confidence strengthens through consistent assumptions, transparency, and explainability. Teams respond faster to market shocks and regulatory changes and reduce operational friction by eliminating manual reconciliation and duplicate analysis.
Taken together, these gains help turn complexity into a strategic asset rather than a constraint.
Why a unified data and AI foundation matters
None of this works in a fragmented environment. Disconnected data flows, duplicated models and inconsistent scenario definitions quickly undermine integration and limit the value of AI in banking.
Modern capabilities depend on a single, scalable foundation for data, analytics, and AI – one that supports governance, reuse, transparency and advanced modeling across all relevant activities.
When risk, finance and treasury teams work from a common analytical foundation, banks can move faster, apply AI more effectively and ensure that insights remain consistent from internal decision‑making through to external reporting.
A strategic capability for the modern bank
Looking ahead, how banks manage and steer assets and liabilities will define their competitiveness and how regulators and markets judge them. What was once a specialist discipline is becoming a core strategic capability, supporting profitability, resilience and trust at the same time.
Multiple shifts are already underway: from siloed analysis to genuinely integrated insight; from static metrics to forward‑looking, scenario‑driven understanding; and from manual interpretation to AI‑supported decision‑making.
In a world defined by uncertainty and rapid change, the ability to steer financial outcomes holistically and intelligently is fast becoming one of the most important differentiators between banks that react and banks that lead. The future is not just about measuring risk. It’s about enabling smarter, faster and more confident decisions across the entire bank.
